Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/srihari-humbarwadi/tensorflow_retinanet
https://github.com/srilakshmiv14/FaceNet_Pytorch
https://github.com/srvcodes/clap4clip
https://github.com/starhiking/ATF
https://github.com/stdereka/knowledge-distillation
An Embarrassingly Simple Approach for Knowledge Distillation
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Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/stergioc/smooth-transformer
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
https://github.com/stevehansen43/progressive_GAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/stevenzhou2017/darknet_AlexeyAB
https://github.com/stresearch/xfr
https://github.com/strivebo/image_segmentation_dl
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/sudhanshu2k/Signature-Verification
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sudharavali/objectDetectionYOLO
https://github.com/suhas1999/Flip-kart-grid-challenge
https://github.com/suhuijia/pggan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/sukibean163/RetinaFace_Paddle
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/sulasen/race-events-recognition-1
https://github.com/sumathi16/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sumitkutty/Anti-Spoof-Face-Recognition
https://github.com/summerice9/AniGAN
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
https://github.com/sungwool/virtual_softmax
https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet
https://github.com/sunil-rival/insightface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/sunjungAn/Tensorflow2.x-CV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sunlizhuang/YOLOv1-PaddlePaddle
https://github.com/sunnysoni97/single_image_fl
https://github.com/superaidou123/face_classification
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/suruoxi/DistanceWeightedSampling
https://github.com/susmith98/Face_Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/susomena/DeepSlowMotion
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/suyash0612/face_recognitionandverification
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/svdesai/heading-date-estimation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/svjack/Detector-in-Detector
https://github.com/sw-gong/spiralnet_plus
4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications
https://github.com/swasun/VQ-VAE-images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.